MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)||7|
|Section||Modelling and Simulation|
|Published online||04 March 2020|
Heavy overload forecasting of distribution transformers based on neural network
1 State Grid Shanghai Electric Power Research institute, Shanghai
2 Shanghai University of Electric Power, Shanghai
* Corresponding author: firstname.lastname@example.org
The overload management is significance component in distribution network operation and maintenance to improve electricity service. According to the periodic characteristics of the electric load, this paper designs a new method to identify and predict the heavy overload states and highlight the dates where the distribution transformer most likely heavy overload through the historical load rate and meteorological data. The Attention-GRU neural network is introduced to predict electric load rate of the highlight dates to improve the prediction efficiency. In comparison with the performances traditional LSTM in prediction of distribution transformers, results show that the new method has higher accuracy and efficiency in predicting highlight dates’ load rates.
Key words: Heavy overload distribution transformer / Efficient forecast / Associated factor / Attention-GRU neural grid
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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